Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

A System for Information Management in BioMedical Studies--SIMBioMS.

UNLABELLED: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented. AVAILABILITY: The source code, documentation and initialization scripts are available at http://simbioms.org.

In silico analyses of mouse inner-ear transcripts.

The development and function of the inner ear is complex requiring the correct and coordinated expression of many genes. The recent progress in the analyses of the human and other genomes has provided tools for identification of genes involved in hearing. As more and more nucleotide sequence information accumulates, experimental methods of molecular biology are rapidly being supplemented, and partially supplanted, by computational methods. In this study we present comprehensive in silico analyses of a cDNA library representing almost 1600 transcripts isolated from mouse inner ear. By mining the public databases we were able to rapidly and efficiently identify numerous transcripts likely to have a specific role in the auditory or vestibular function of the inner ear. Analyses revealed about 600 known genes and almost 100 inner-ear specific transcripts. Almost 50 of these are candidate genes for hearing impairment based on their chromosomal localization and inner-ear expression pattern. We describe a powerful approach to identify novel genes associated with hearing and vestibular function, further increasing our understanding of the molecular biology of the inner ear.